Acute myeloid leukemia (AML) is the most common type of adult hematopoietic malignancy. It has a high rate of disease relapse, a consequence of chemoresistance. Recent biological studies suggest that a major component of the relapse phenotype resides in a rare population of leukemic stem cells (LSC), characterized by extensive proliferative and self-renewal potential, and poor response to standard chemotherapeutic agents. We hypothesize that i) common molecular characteristics inherent to LSC-enriched populations reflect the biology of heterogeneous AML with unfavorable prognostic features; ii) these associations can be derived from systematic analysis of gene and microRNA expression profiles, and other biologic characteristics of bulk AML samples. The overarching goal of this project is to define LSC-characterized transcription factor (TF) and microRNA interactions in heterogeneous AMLs. We proposed to integrate both qualified data-driven and curated knowledge about LSC characteristics, and clinical outcomes from bulk AML. Specifically, we will perform meta-analyses on a pathway level to build LSC-specific biology, focusing on AML prognostic TF/microRNA deregulation. These approaches are supported by our two pioneering mathematical methodologies: the Functional Analysis of Individual Microarray Expression (FAIME) and the mechanism-anchored Phenotypes-Genotype Network (PGNet). FAIME provides a novel process for transforming extensive available gene expression profiles into individual pathway profiles, resulting in more reproducible pathway signatures (46% overlap among three cohorts, empirical p<0.001). The PGNet method reveals genes regulated by disease-critical regulators and can accurately predict patient outcomes - shifting the paradigm from single gene/microRNA analysis towards mechanism anchored profiling. Using PGNet, we have successfully predicted that the epigenetic regulator HDAC9 is associated with survival in acute lymphoblastic leukemia. Innovatively, this project will interrogate microRNAs/genes that regulate LSC-specific biological pathways and AML prognostication, integrating regulator-regulator interactions and regulator-gene interactions. In Aim 1, we will build LSC-specific gene pathways and identify regulator-gene interactions. In Aim 2, we will correlate LSC-specific regulators and their target genes corresponding to AML outcomes. In Aim 3, we will develop novel approaches to computationally model the crucial interactions among the LSC-specific regulators and prognostic gene targets. Both the abundance of available AML patient profiles and the proven ability of our proposed methods suggest that we will achieve our aim to build an LSC-driven prognostic model.